the effect of education on life satisfaction across countries

The Alberta Journal of Educational Research Vol. 55, No. 1, Spring 2009, 124-136

Hoi Yan CheungCity University of Hong Kong

and

Alex W.H. ChanUniversity of Hong Kong

The Effect of Educationon Life Satisfaction Across Countries

The study of life satisfaction of diverse countries is becoming increasingly important.Studies have shown that people who are satisfied with their lives are positive about otheraspects of their lives such as their health. So it is important to examine the factors that canlead to life satisfaction. This study examines the relationships between education and lifesatisfaction across countries. Thirty-five countries are included in the study; the resultsshow that life satisfaction is higher in countries where people have more education. Years ofeducation are predicted by enrollment rates at the secondary and tertiary levels. Based onthe findings of this study, educators and policy-makers should encourage people tocontinue their education.

L’étude de la satisfaction de vivre dans divers pays prend de l’importance. La recherche adéjà indiqué que ceux qui sont satisfaits de leur vie sont également plus positifs face àd’autres aspects de leur vie comme leur santé. Il est donc important d’étudier les facteursqui entraînent la satisfaction de vivre. Ce projet porte sur le rapport entre l’éducation et lasatisfaction de vivre dans trente-cinq pays. Les résultats indiquent que la satisfaction devivre est plus élevée dans les pays présentant des taux de scolarité plus élevés. Les annéesde scolarité sont évaluées en fonction des taux d’inscription aux niveaux secondaire ettertiaire. Les résultats de cette étude poussent les auteurs à recommander que lesenseignants et les décideurs encouragent les gens à poursuivre leur éducation.

Quality of life (QOL) has been much studied. QOL in its simplest form can beunderstood as what constitutes a good life. According to Ventegodt et al.(2005), QOL is divided into subjective QOL, which includes well-being, satis-faction with life, happiness, and meaning in life; and objective QOL, whichconsists of biological order, realization of life potential, fulfillment of needs,and objective factors such as cultural norms. However, only in the last fewdecades have systematic and empirical studies of QOL been conducted. As aresult of these, the concept of QOL has been changed from being a judgmentthat depends on subjective values to being regarded as consisting of the samecomponents for all people (Lyons, 2005). Bramston, Chipuer, and Pretty (2005)deemed QOL to be multidimensional, with personal factors, environmental

Hoi Yan Cheung is an assistant professor in the Department of Applied Social Studies. Herresearch interests are comparative studies, cross-cultural studies, and educational psychologytopics such as teacher efficacy. Most of her studies have involved comparing educational aspectsamong countries and cultures.Alex Chan is an assistant professor in the School of Economics and Finance in the Faculty ofBusiness and Economics. His research interests are the areas of finance, economics, and education.

124

AJER Journals Spring 09.indd 130 3/31/09 8:36:53 PM

factors, and the interaction between the two playing important roles in deter-mining people’s overall QOL.

As mentioned, a major component of QOL is life satisfaction. Frisch et al.(2005) assert,

Quality of life is often equated with life satisfaction in psychology andpsychiatry … when quality of life is not equated with life satisfaction, lifesatisfaction is usually included as an essential component of a quality of lifebattery of assessment. (p. 67)

They define life satisfaction as people’s subjective evaluation of how far theirmost important needs, goals, and wishes have been fulfilled. Diener (1994)defines life satisfaction as the global evaluation by people of their own life.

Compared with the concept of well-being, life satisfaction is less em-phasized in the research. Furthermore, most of the studies that examine lifesatisfaction have focused on adults (Diener, 1994; Veenhoven, 2000) such asundergraduate students (Benjamin & Hollings, 1997; Disch, Harlow, Campbell,& Dougan, 2000; Wells, 1998) and older age groups (Morganti, Nehrke,Hulicka, & Cataldo, 1988). Many instruments have been developed to try tounderstand people’s conception of their level of life satisfaction such as theSatisfaction with Life Scale (Diener, Horwitz, & Diener, 1995; Pavot & Diener,1993), the Subjective Well-Being Inventory (SWBI, Nagpal & Sell, 1985; Sell &Nagpal, 1992), the life satisfaction measure in the Quality of Life Inventory(QOLI, Frisch, 2004), the Life Satisfaction Matrix (LSM, Lyons, 2005), and theBrief Multidimensional Students’ Life Satisfaction Scale (BMSLSS, Zullig,Huebner, Gilman, Patton, & Murray, 2005).

Generally speaking, a low level of life satisfaction is associated with nega-tive events or incidents both at the individual and location levels. Moore,Leslie, and Lavis (2005) found that experiences of negative emotions and well-being were associated with a lower level of life satisfaction, and Zullig et al.(2005) showed a higher level of life satisfaction rating to be negatively corre-lated with sadness, depression, worry, anxiety, and various negative physicaland mental conditions. Ratings of life satisfaction are judgments that relystrongly on an individual’s positive experiences, so a low level of life satisfac-tion may be a major risk factor for psychological disturbance (Lewinsohn,Redner, & Seeley, 1991).

Whether education can increase people’s QOL is an important anddebatable topic. An interesting study by Hillman and McMillan (2005) showedthat the life satisfaction of Australian youths was significantly but weaklyassociated with their participation in a range of post-school education, training,and labor market activities. The study was subsequently used in the design ofvarious post-school activities to enhance the life satisfaction of students and isa good example of how educational aspects can directly affect overall lifesatisfaction. Other studies in this area mainly focus on how educational aspectsaffect the various domains of satisfaction.

Davis and Friedrich (2004) found that older people with a higher level ofeducation rated their well-being higher. This may be because the knowledgethat older people have gained from their education has prepared them better toadapt to the physical, psychological, and social changes of aging. Education

The Effect of Education on Life Satisfaction

125


affects various areas of satisfaction. For example, the level of education ofemployees was found to be related to their self-assessed satisfaction withvarious aspects of their job when the characteristics of the employees werecontrolled (Vila & García-Mora, 2005). Although the study focused only on thejob satisfaction of employees, the level of overall life satisfaction is believed tobe derived from satisfaction in various domains of life (Lyubomirsky, King, &Diener, 2005).

When the level of life satisfaction is assumed to derive from a combinationof levels of satisfaction in various domains in life, the study of satisfaction witheducation becomes important, especially because people spend many years oftheir lives in school. Satisfaction with education has been found to be as-sociated with persistence, graduation, grade achievement, alumnus status, andmany other positive aspects and experiences (Einarson & Matier, 2005). Einar-son and Matier indicate that the study of satisfaction and education peakedbetween the 1970s and the mid-1980s. However, most of the studies conductedat this time focused either on the satisfaction of students with a particularinstitute or on a particular race or ethnicity, and the findings cannot be gener-alized because institutes and ethnicities may generate differing levels of satis-faction caused by diverse variables. The investigation of the effect of variousareas of education on overall life satisfaction, therefore, remains important, asresearch on this topic is limited.

If education really affects life satisfaction as some studies have claimed, it isimportant to offer quality education and to encourage more people to studyformally. Offering quality education and increasing enrollment have beenfound to be affected by government expenditure on education (Jones & Zim-mer, 2001). Cheung and Chan (2008) showed that education expenditurepredicts the enrollment rate and quality of education across countries. Otherstudies have found that education expenditure is determined by governmentsand authorities (Falch & Rattso, 1999) and also a country’s cultural dimensions(Cheung & Chan). Although the government of each region decides the specificamount that will be spent on education, the real budget increases or decreasesin line with the amount that is earned by a country each year, or the GDP.

Methods of Measuring Life SatisfactionThere are two approaches to the determination of QOL: the top-down ap-proach and the bottom-up approach (Kahneman, 1999). People generally applyeither or both approaches to construct their satisfaction judgment (Leonardi,Spazzafumo, & Marcellini, 2005). According to Diener (1984), the top-downapproach assumes that there is “a global propensity to experience things in apositive way, and [that] this propensity influences the momentary interactionsan individual has with the world” (p. 565). In other words, the top-downapproach mainly focuses on how people’s personalities affect their QOL. Incontrast, the bottom-up approach emphasizes the effects of external events,situations, and demographics on QOL (Sousa-Poza & Sousa-Poza, 2000). Theunderlying principle of the bottom-up approach is that when basic and univer-sal human needs are met, then the QOL rating will be higher. Sousa-Poza andSousa-Poza applied the bottom-up approach by using work-role inputs (edu-cation, working time, exhausting job, and physically demanding anddangerous job) to measure the workplace well-being of employees across coun-

H.Y. Cheung and A.W.H. Chan

126


tries and found that most work-role inputs had a significant effect on well-being.

Aims of the StudyThis study aimed to find out if merely participating in various levels of educa-tion can lead to higher overall life satisfaction. In an effort to fill this gap, thisstudy takes the bottom-up approach to study life satisfaction and its rela-tionship with education in 35 countries. The eight educational variables in-cluded are the overall enrollment rate of students in primary, secondary, andtertiary education; the enrollment rate of female students in primary, secon-dary, and tertiary education; and the expected number of years that will bespent in all three levels of education. The study also aims to determine whetherthe percentage of GDP that is spent on education affects the seven educationalvariables and thus the rating of life satisfaction across the countries.

MethodologyThe dataset shown in Table 1 was collated from various sources. The data onlife satisfaction across countries were taken from the Average Happiness in 91Nations 1995-2005, World Database of Happiness Rank Report. This reportmeasured the level of happiness in 91 countries from 1995 to 2005 and includeslife satisfaction as a category, which was assessed by means of surveys amongsamples of the general population of the countries (Veenhoven, 2006). Thescores are based on responses to the question All things considered, how satisfiedor dissatisfied are you with your life as a whole now? which were rated on anumerical scale that ranged from 0—dissatisfied to 10—satisfied.

Several other studies have used data from the World Database of Happiness.Heylighen and Bernheim (2000) point out that the World Database of Happinesshas compiled the results of hundreds of surveys to test something similar toglobal QOL. The data were collected by various institutions in the countriesusing varied methodologies, but the results are comparable because they werecompiled to a common standard; and although the quality of the input datavaries somewhat, no systematic biases can be found in the methodology. Interms of the question All things considered, how satisfied or dissatisfied are you withyour life as a whole now? answers are generally comparable to the results of anACSA (Anamnestic Comparative Self-Assessment) score of global QOL(Bernheim, 1999; Bernheim & Buysc, 1984).

The data on the six education variables (overall primary, secondary, andtertiary enrollment and female primary, secondary, and tertiary overall enroll-ments) were collected from the World Development Indicator (WDI) 2006database (World Bank, 2006). The database aims to provide quality statistics(both national and international) to improve the capacity of member countriesto produce and use statistical information for studies and to enhance theirdevelopment. As the titles of the variables suggest, primary, secondary, andtertiary female enrollment measure the enrollment rates of women in eachcountry at these three educational levels; and overall primary, secondary, andtertiary enrollment measure the overall enrollment rates at these three educa-tional levels.

The data on expected number of years of education were collected from theWorld Education Indicators Programme 2005 (UNESCO, 2005). Expected years of


127


education is the total number of years of education for both men and women ineach country for all levels of education (primary and lower secondary educa-tion, upper secondary education, postsecondary non-tertiary education, andtertiary education) excluding children under the age of 5. The dataset shown inTable 1 combines data from the Average Happiness in 91 Nations 1995-2005from the World Database of Happiness Rank Report, the WDI 2006 database, andthe World Education Indicators Programme 2005 for the 35 countries under study.

Table 1Dataset for the 35 Countries

Primary Secondary Tertiary Primary Secondary Tertiary Education Expectancy

Life overall overall female female female female expenditure years of

satisfaction enrollment enrollment enrollment enrollment enrollment enrollment based on GDP education

1995-2005 2003 2003 2003 2003 2003 2003 2000-2002 2002-2003

Australia 7.70 101.67 153.84 73.97 1.00 .97 1.23 4.90 21.10

Austria 7.80 104.72 100.35 48.71 .99 .95 1.18 5.70 16.10

Belgium 7.30 104.90 160.15 60.67 .99 1.10 1.19 6.30 19.70

CzechRepublic 6.40 102.15 96.89 36.88 .98 1.03 1.07 4.40 16.60

Denmark 8.20 102.63 127.31 66.83 1.00 1.05 1.42 8.50 18.30Egypt,Arab Rep. 4.80 100.48 86.88 28.53 .95 .93 n/a n/a 12.00

Finland 7.70 101.66 127.39 86.90 .99 1.11 1.20 6.40 19.70

France 6.50 105.21 109.99 55.35 .99 1.01 1.28 5.60 16.80

Germany 7.20 99.39 100.07 50.10 1.00 .98 1.00 4.60 17.20Greece 6.40 99.98 95.58 72.24 1.00 1.02 1.14 4.00 16.50

Hungary 5.70 98.55 103.41 51.89 .99 1.00 1.37 5.50 17.20

Iceland 7.80 100.90 114.72 61.70 .98 1.06 1.78 6.00 19.20

India 5.70 107.43 52.29 11.50 .94 .79 .67 4.10 9.80

Indonesia 6.60 116.16 61.82 16.23 .98 .99 .80 1.20 11.90Ireland 7.60 105.57 109.02 55.29 .99 1.09 1.31 5.50 16.70

Italy 6.90 100.97 99.09 59.02 .99 .99 1.34 4.70 16.80Japan 6.20 100.43 102.00 52.13 1.00 1.00 .88 3.60 n/aJordan 5.20 99.90 88.29 34.98 1.01 1.02 1.10 n/a 12.60Luxemburg 7.60 99.18 96.01 12.39 .99 1.06 1.18 n/a 14.80

Mexico 7.70 109.21 78.83 22.49 .98 1.08 .97 5.30 13.20Netherlands 7.50 107.92 121.94 58.00 .98 .99 1.09 5.10 17.30

New Zealand 7.40 102.19 118.93 71.58 1.00 1.12 1.47 6.70 18.60

Norway 7.60 99.49 113.84 80.30 1.00 1.02 1.55 7.60 18.20

Philippines 6.40 112.54 83.94 29.40 .99 1.10 1.28 3.10 11.80

Poland 5.90 99.51 104.51 59.47 .99 .96 1.42 5.60 17.20Portugal 6.00 118.48 109.04 55.53 .95 1.09 1.35 5.80 16.90

RussianFederation 4.30 117.51 92.95 65.23 .99 1.00 1.35 3.80 14.90SlovakRepublic 5.40 100.25 91.73 33.99 .99 1.01 1.18 n/a 15.30

Spain 6.80 107.45 116.52 63.55 .98 1.05 1.19 4.50 17.00

Sweden 7.70 109.11 137.03 81.78 1.03 1.19 1.55 7.70 20.10Switzerland 8.10 102.68 92.93 44.97 1.00 .92 .77 5.80 16.70

Turkey 5.30 94.69 85.30 28.01 .94 .75 .75 3.70 12.00

UnitedKingdom 7.10 100.82 170.12 62.76 1.00 1.25 1.30 5.30 20.40

United States 7.40 99.68 94.53 82.59 1.00 1.00 1.37 5.70 16.80

Zimbabwe 3.30 96.02 36.36 3.67 .98 .91 .63 4.70 11.30


128


Finally, the data on the percentage of GDP spent on education were col-lected from the Human Development Report 2005, which is published by theUnited Nations Development Programme (UNDP, 2005). Over the years,Human Development Reports have received an excellent worldwide reputa-tion and have played a crucial role in understanding and setting key develop-ment policies. The 2005 Report has many sections, but this study uses theCommitment to Education: Public Spending section to derive the data for thisvariable. A major reason for selecting these datasets is because they werecompiled by well-known and reputable organizations such as UNESCO andthe World Bank.

ResultsThe correlation results for the nine variables show that life satisfaction issignificantly correlated with all the other variables except overall primaryenrollment. The highest two correlations were between expected years of edu-cation and overall secondary and overall tertiary enrollment, with r=.89 andr=.84 respectively. Table 2 shows the correlation results for the variables.

Regression analysis was conducted to predict the level of life satisfaction inthe 35 countries. All models applied the stepwise method. First, all the vari-ables (overall primary, secondary, and tertiary enrollment; female primary,secondary, and tertiary female enrollment; expected years of education; andpercentage of GDP spent on education) were entered as independent variables.Table 3 shows that only expected years of education was included in the finalmodel and that it predicted 36% of the total variance in life satisfaction with apositive slope at the p<.001 significance level.

To determine the factors that predict expected years of education, overallprimary, secondary, and tertiary enrollment; female primary, secondary, andtertiary enrollment; and percentage of GDP spent on education were entered asindependent variables. Table 4 shows that overall secondary and tertiary en-rollment were included in the final model as both were able to predict 89% ofthe total variance in expected years of education with positive slopes at thep<.001 level.

Table 2Correlations Among the Nine Variables

Variables (N=35) 1 2 3 4 5 6 7 8 9

1. Life satisfaction -

2. Primary overall enrollment .03 -3. Secondary overall enrollment .57** –.03 -

4. Tertiary overall enrollment .48** –.01 .73** -

5. Primary female enrollment .41* –.13 .43** .52** -

6. Secondary female enrollment .43** .26 .62** .46** .57** -7. Tertiary female enrollment .38* .07 .59** .70** .42** .63** -

8. Expectancy years of education .63** –.12 .89** .84** .56** .59** .67** -

9. Education expenditure GDP .50** –.25 .53** .59** .39* .38* .58** .65** -

Note. *Correlation is significant at the .05 level (2-tailed);**Correlation is signiicant at the .01 level (2-tailed).


129


To predict the rate of overall secondary and tertiary enrollment, femaleprimary, secondary, and tertiary enrollment, and percentage of GDP spent oneducation were entered as independent variables. Table 5 shows that femalesecondary enrollment and percentage of GDP spent on education predicted49% of the total variance in overall secondary enrollment. The slopes werepositive, and female secondary enrollment and percentage of GDP spent oneducation were significant at the p<.01 and p<.05 levels respectively. Table 6shows that female primary and female tertiary enrollment predicted 63% of thetotal variance in overall tertiary enrollment. The slopes were positive, and

Table 3Summary of Regression Analysis for Variables Predicting Life Satisfaction

(N=35)

Variable B SE B β

Expectancy years of education .24 .06 .60***

Note. R2=.36 and Adj. R2=.33 (p<.001).

Table 4Regression Analysis for Variables Predicting Expectancy Years of Education

(N=35)

Variable B SE B β

Secondary overall enrollment .06 .01 .60***Tertiary overall enrollment .05 .01 .41***

Note. R2=.89 and Adj. R2=.88 (p<.001).

Table 5Regression Analysis for Variables Predicting Secondary Overall Enrollment

(N=35)

Variable B SE B β

Secondary female enrollment 142.29 41.58 .50**Education expenditure based on GDP 6.70 2.86 .34*

Note. R2=.49 and Adj. R2=.46 (p<.001).

Table 6Regression Analysis for Variables Predicting Tertiary Overall Enrollment

(N=35)

Variable B SE B β

Tertiary female enrollment 45.69 9.80 .60***Primary female enrollment 386.16 150.18 .33*

Note. R2=.63 and Adj. R2=.61 (p<.001).


130


female primary and tertiary enrollment were significant at the p<.001 and p<.05levels respectively.

Finally, the percentage of GDP spent on education predicted 15%, 14%, and34% of the female primary, secondary, and tertiary enrollment respectively.Tables 7, 8, and 9 show the respective results. All three models had positiveslopes, and female primary, secondary, and tertiary enrollment were sig-

Table 7Regression Analysis for Variables Predicting Primary Female Enrollment

(N=35)

Variable B SE B β

Education expenditure based on GDP .01 .002 .39*

Note. R2=.15 and Adj. R2=.13 (p<.001).

Table 8Regression Analysis for Variables Predicting Secondary Female Enrollment

(N=35)

Variable B SE B β

Education expenditure based on GDP .03 .01 .38*

Note. R2=.14 and Adj. R2=.11 (p<.001).

Table 9Regression Analysis for Variables Predicting Tertiary Female Enrollment

(N=35)

Variable B SE B β

Education expenditure based on GDP .11 .03 .58***

Note. R2=.34 and Adj. R2=.31 (p<.001).

Figure 1. Relationships among the variables.


131

Education and Life Satisfaction 28

Figure 1

Relationships amongst the Variables

Secondaryfemale enrollment

Primary female enrollment

Tertiary female enrollment

Secondaryoverallenrollment

Lifesatisfaction

Expectancyyears of education

Tertiary overall enrollment

Educationexpenditurepercentagebased on GDP


nificant at the p<.05, p<.05, and p<.001 levels respectively. Figure 1 summarizesthe relationships between the variables.

DiscussionA regression analysis model of this study shows that expected years of educa-tion seems to be the strongest predictor of life satisfaction across the 35 coun-tries. Thus based on this result, it is logical to find that the overall secondaryand tertiary enrollments are able to predict the expected years of education. Inother words, the longer the time spent in education, the higher is people’s levelof life satisfaction. In this section we discuss some of the possible explanationsfor the effects of education on life satisfaction. According to Maslow’s (1970)Need-Gratification Model of Subjective Well-Being, in which lower needs(such as physiological, safety, and love needs) must be met first, education isconsidered a higher need. It is believed that the gratification of higher needsproduces greater happiness and life satisfaction than the gratification of lowerneeds (Oishi, Diener, Lucas, & Suh, 1999), but to enjoy more years of education,people’s basic needs must first be met, which will no doubt lead them to feelmore satisfied with their lives in some way. Moreover, education or knowledgeis important in meeting higher needs such as self-respect, freedom, confidence,and self-actualization (Maslow).

It is interesting that overall primary enrollment did not predict expectedyears of education, which indicates that the emphasis in terms of life satisfac-tion is on secondary and tertiary education levels. The World Declaration onEducation for All and the Framework for Action to Meet Basic Learning Needs(World Bank, 1994) urge governments to universalize primary education andsignificantly reduce illiteracy before the end of the decade. In addition, underthe Dakar Framework for Action, all member countries have committed toseveral obligations, one of which is to ensure that by 2015 all children, andparticularly girls, children in difficult circumstances, and those belonging toethnic minorities have access to and complete free and compulsory qualityprimary education (UNESCO, 2000). Governments around the world havebeen urged to give the highest policy and budgetary priority to improving theireducation systems so that all children can receive a basic education.

According to Maslow’s (1970) Theory of Needs, academically speaking,primary education is a basic need. Most countries are now monitored byvarious organizations such as the World Bank and UNESCO to ensure thatthey offer basic primary education to all children. However, it may be that onlywell-off countries that respect human rights offer secondary education. Peoplein a given country who are able to obtain more than their basic needs may bemore satisfied than those who are able to meet only their basic needs.

Female primary, secondary, and tertiary enrollments were significant inde-pendent variables in predicting overall secondary and tertiary enrollment.Often children who are excluded from education are believed to experiencegender and racial discrimination, disability, poverty, geographical remoteness,or political and economic turmoil. According to data from the Global MonitoringReport (UNESCO, 2006), a major barrier for children who are not enrolled inschool is lack of gender parity, which can be defined as a state of formalequality between men and women in terms of access to, and participation in,education. The Global Monitoring Report also mentions that one of the Millen-


132


nium Development goals is the achievement of gender parity in primary andsecondary education by 2005 and the extension of such parity to all levels ofeducation by 2015. It is, therefore, necessary to achieve the equal participationof girls and boys in all levels of education proportionate to their share in therelevant age groups of the population. However, according to the GlobalMonitoring Report (UNESCO, 2004), 54 countries are at risk of not achieving thisgoal based on current trends. More than 56% of children who are not in schoolare girls, and over two thirds of illiterate people globally are women. In thisstudy the female enrollment rates affected the overall enrollment rates becausewhen more women are enrolled in education, the overall enrollment rate alsoincreases.

Finally, the results of this study also show that the percentage of GDP spenton education significantly predicts the female enrollment rates at all threeeducational levels. Many studies have shown a positive relationship betweeneducation expenditure and enrollment (Gordon, 1982; Leal & Hess, 2000).Similarly, in this study the correlations between percentage of GDP spent oneducation and the enrollment rate at the various levels of education weresignificant (ranging from r=.38-.58) except for overall primary enrollment.Whetzel and McDaniel (2006) demonstrate that the knowledge of a populationis an important and significant factor that determines differences in wealth andeconomic growth across countries, and thus the amount that is spent on educa-tion. Therefore, when people study for longer and gain more knowledge as aresult, this knowledge will eventually serve to increase the economic growth oftheir country. Another study has shown that graduates who study on four-yeardegree courses earn twice as much as graduates with no degree and are able tofind jobs more easily, and that even degree-holders who study for only twoyears have a higher QOL than those with only a high school diploma. The lifesatisfaction of more educated people may be higher because they enjoy a betterstandard of living and have a more stable life. Interestingly, it requires moretime for first-generation college students to earn as much as other students(Adam, 2006).

We do not wish overinterpret the reasons why education predicts life satis-faction, but to provide some possible reasons for the relationships between theeducation variables. The principle of the bottom-up approach is that whenbasic human needs are met, QOL increases. The results of this study are in linewith this principle in that greater education expenditure, more years of educa-tion, and higher enrollment rates are found to lead to a higher level of lifesatisfaction. In conclusion, it may be important to investigate and understandwhat other affecting factors could influence the nine variables examined in thisstudy. Because education expenditure and policies for primary, secondary, andtertiary enrollments are linked to the decision of countries’ governors andpolicy-makers, culture may play an important role in this issue.

Limitations and Further StudiesOverall, the results of this study could only capture the situation of howenrollments and education expenditure affect the life satisfaction across 35countries. As mentioned above, other factors, for example, culture, may in-fluence the variables that were examined in this study. Therefore, in futurestudies it would be meaningful and interesting to examine how cultural


133


dimensions affect education enrollment, growth in GDP per capita, and lifesatisfaction across countries, for example, by investigating whether these vari-ables are affected by Hofstede’s cultural dimensions (power distance, in-dividualism, uncertainty avoidance, and masculinity). As every country has itsown group of cultural dimensions and it is believed that culture and values areimportant to all people, this issue merits further examination.

Because countries for which there are no data on the nine variables (lifesatisfaction; overall primary, secondary, and tertiary enrollment; femaleprimary, secondary, and tertiary enrollment; expected years of education, andpercentage of GDP spent on education) were excluded from this study, only 35countries were analyzed. The results were generated from these 35 countriesand therefore cannot be generalized. However, this study is a first step inestablishing that the inclusion of more people in education predicts life satis-faction across countries.

Note

Correspondence about this article should be addressed to Hoi Yan Cheung, Department ofApplied Social Studies, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong.E-mail: [email protected]

ReferencesAdam, M. (2006). Does higher education count less? Education Digest, 71(6), 47-51.Bernheim, J. (1999). How to get serious answers to the serious question: “How have you been?”:

Subjective quality of life (QOL) as an individual experiential emergent construct. Bioethics, 13,3-4.

Bernheim, J., & Buysc, M. (1984). Anamnestic comparative self-assessment (ACSA): A method tomeasure the subjective quality of life of cancer patients. Journal of Psychosocial Oncology, 1,25-38.

Benjamin, M., & Hollings, A. (1997). Life satisfaction: Test of an ecology model. Journal of CollegeStudent Development, 31, 213-228.

Bramston, P., Chipuer, H., & Pretty, G. (2005). Conceptual principles of quality of life: Anempirical exploration. Journal of Intellectual Disability Research, 49, 728-733.

Cheung, H.Y., & Chan, A.W.H. (2008). Relationships amongst cultural dimensions, educationalexpenditure and class size of different nations. International Journal of Educational Development,28, 698-707.

Davis, N.C., & Friedrich, D. (2004). Knowledge of aging and life satisfaction among older adults.International Journal of Aging and Human Development, 59(1), 43-61.

Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95, 542-575.Diener, E. (1994). Assessing subjective well-being: Progress and opportunities. Social Indicators

Research, 31, 103-157.Diener, E., Horwitz, J., & Diener, C. (1995). Factors predicting the subjective well-being of

nations. Journal of Personality and Social Psychology, 5, 851-864.Disch, W.B., Harlow, L.L., Campbell, J.F., Dougan, T.R. (2000). Student functioning, concerns,

and socio-personal well-being. Social Indicators Research, 51, 41-74.Einarson, M.K., & Matier, M.W. (2005). Exploring race differences in correlates of seniors’

satisfaction with undergraduate education. Research in Higher Education, 46, 641-676.Falch, T., & Rattso, J. (1999). Local public choice of school spending: Disaggregating the demand

function for educational services. Economics of Education Review, 18, 361-373.Frisch, M.B. (2004). Use of the QOLI or quality of life inventory in quality of life therapy and

assessment. In M.R. Marusih (Ed.), The use of psychological testing for treatment planning andoutcome assessment (pp. 749-798). Englewood Cliffs, NJ: Erlbaum.

Frisch, M.B., Clark, M.P., Rouse, S.V., Rudd, M.D., Paweleck, J.K., Greenstone, A., & Kopplin,D.A. (2005). Predictive and treatment validity of life satisfaction and the quality of lifeinventory. Assessment, 12(1), 66-78.

Gordon, A. (1982). Resource redemployment in education in the 1980’s: Problems and prospects.Educational Studies, 8(1), 55-77.


134


Heylighen, F., & Bernheim, J. (2000). Global progress I: Empirical evidence for ongoing increasein quality-of-life. Journal of Happiness Studies, 1, 323-349.

Hillman, K., & McMillan, J. (2005). Life satisfaction of young Australians: Relationships between furthereducation, training and employment and general and career satisfaction: Longitudinal surveys ofAustralian youth research report 43. Victoria: Australian Council for Education Research Press.

Jones, J.T., & Zimmer, R.W. (2001). Examining the impact of capital on academic achievement.Economics of Education Review, 20, 577-588.

Kahneman, D. (1999). Objective happiness. In D. Kahneman, E. Diener, & N. Schwarz (Eds.),Well-being (pp. 3-26). New York: Russell Sage Foundation.

Leal, D.L., & Hess, F. (2000). The politics of bilingual education expenditures in urban schooldistricts. Social Science Quarterly, 81, 1064-1072.

Leonardi, F., Spazzafumo, L., & Marcellini, F. (2005). Subjective well-being: The constructionistpoint of view. A longitudinal study to verify the predictive power of top-down effects andbottom-up processes. Social Indicators Research, 70(1), 52-77.

Lewinsohn, P., Redner, J., & Seeley, J. (1991). The relationship between life satisfaction andpsychosocial variables: New perspectives. In F. Strack, M. Argyle, & N. Schwartz (Eds.),Subjective well-being (pp. 141-169). New York: Plenum.

Lyons, G. (2005). The life satisfaction matrix: An instrument and procedure for assessing thesubjective quality of life of individuals with profound multiple disabilities. Journal ofIntellectual Disability Research, 49, 766-769.

Lyubomirsky, S., King, L., Diener, E. (2005). The benefits of frequent positive affect: Doeshappiness lead to success? Psychological Bulletin, 131, 803-855.

Maslow, A.H. (1970). Motivation and personality. New York: Harper & Row.Moore, S.E., Leslie, H.Y., & Lavis, C.A. (2005). Subjective well-being and life satisfaction in the

Kingdom of Tonga. Social Indicators Research, 70, 287-311.Morganti, J.B., Nehrke, M.F., Hulicka, I.M., & Cataldo, J.F. (1988). Life-span differences in life

satisfaction, self-concept, and locus of control. International Journal of Aging and HumanDevelopment, 26, 45-56.

Nagpal, R., & Sell, H. (1985). Subjective well-being. New Delhi: World Health OrganizationRegional Office for South East Asia.

Oishi, S., Diener, E.F., Lucas, R.E., & Suh, E.M. (1999). Cross-cultural variations in predictors oflife satisfaction: Perspectives from needs and values. Personality and Social Psychology Bulletin,25, 980-990.

Pavot, W., & Diener, E. (1993). Review of the satisfaction with life scale. Psychological Assessment,5, 164-172.

Sell, H., & Nagpal, R. (1992). Assessment of subjective well-being: The subjective well-being inventory(SUBI). New Delhi: World Health Organization, Regional Office for South-East Asia.

Sousa-Poza, A., & Sousa-Poza, A.A. (2000). Well-being at work: A cross-national analysis of thelevels and determinants of job satisfaction. Journal of Socio-Economics, 29, 517-538.

UNDP. (2005). Human development report 2005. New York, Author.UNESCO. (2000). The Dakar framework for action. Paris: Office of the Assistant Director-General for

Education, UNESCO.UNESCO. (2004). Global monitoring report 2003-2004. Gender and education for all: The leap to

equality. Paris: Author.UNESCO. (2005). World education indicators programme 2005. Paris: Author.UNESCO. (2006). (2006). Education for all global monitoring report—Literacy for life. Paris: Author.Veenhoven, R.A. (2000). The four qualities of life: Ordering concepts and measures of the good

life. Journal of Happiness Studies, 1, 1-39.Veenhoven, R. (2006). Average happiness in 91 nations 1995-2005, World database of happiness,

RankReport 2006. Retrieved January 8, 2007, from: worlddatabaseofhappiness.eur.nlVentegodt, S., Flensborg-Madsen, T., Niels, J., Nielsen, M., Mohammed, M., & Merrick, J. (2005).

Global quality of life (QOL), health and ability are primarily determined by ourconsciousness: Research findings from Denmark 1991-2004. Social Indicators Research, 71(1-3),87-122.

Vila, L.E., & Garcia-Mora, B. (2005). Education and the determinants of job satisfaction. EducationEconomics, 13, 409-425.

Wells, L. Jr. (1998). The effects of ethnicity on the quality of student life: An embedded intergroupanalysis. Journal of Applied Behavioral Science, 34, 403-417.

Whetzel, D.L., & McDaniel, M.A. (2006). Prediction of national wealth. Intelligence, 34(5), 449-458.World Bank (1994). World conference on education for all: Meeting basic learning needs. New York:

Inter-Agency Commission.


135


World Bank. (2006). The world development indicator 2006. New York: Author.Zullig, K.J., Huebner, E.S., Gilman, R., Patton, J.M., & Murray, KA. (2005). Validation of the brief

multidimensional students’ life satisfaction scale among college students. American Journal ofHealth Behavior, 29, 206-214.


136


the effect of education on life satisfaction across countries

Documents